Archive for category The state of the economy
The Other Cliff — Regulations
Posted by TonyLima in Economic policy, The state of the economy on December 22, 2012
About two years and nine months ago I wrote an article pointing out the limitations of conventional fiscal policy analysis. To summarize, there is more to fiscal policy than G, TA, and TR (government spending, taxes, and transfer payments for those a little rusty on their macroeconomics). Government regulations also have a significant impact on the economy. Now that the election is behind us, the economy has fallen off the other cliff — regulations. This article will give a few examples, but the main point is in the table above (from the handy government website Regulations.gov on December 22, 2012). Almost 6,000 new regulations have been posted in the last 90 days. Half of those (3,148 to be precise) have been posted since the election. (Click the “Last 90 days” link, then look for the “custom date range” filter and you can play along, too.) If you believe that’s not going to have a significant impact on businesses, then you probably also believe the unemployment rate was under 7 percent last month.
Regulations, Rules, and Hiring, Oh, My
Sometimes the impact of these regulations can be seen easily. For example, the CAFE (corporate average fuel economy) standards imposed on the U.S. auto industry has done as much as anything to distort the auto market. Since CAFE standards do not apply to light trucks, automakers in the U.S. have been pushing them like mad. High margins are maintained with the help of a nifty 25% tariff on imported light trucks. A somewhat offsetting benefit is that some foreign car makers have set up assembly lines in the U.S., increasing U.S. employment. Trust me, this will not offset the harm done by the tariff.
In other cases, the regulations have a less specific impact. Most economists now agree that a significant contribution to the high unemployment rate in the U.S. has been uncertainty over the rules and regulations that will be required by the Patient Affordable Care Act [sic] (ACA) and the Dodd–Frank Wall Street Reform and Consumer Protection Act. Uncertainty means risk and businesses — especially small businesses — are very risk-averse. Some of the impacts are already being seen. For example, one ACA rule says that part-time employees who work less than 30 hours per week are exempt from ObamaCare rules. The result has been entirely predictable: businesses that rely on part-time employees are cutting their hours. One highly publicized example is Papa John’s Pizza. Darden Restaurants owns the Olive Garden and Red Lobster chains. They, too, are moving toward limiting part-time employees to less than 30 hours per week.
And consider the marginal cost of hiring employee number 50. “Businesses with 50 or more full-time workers must pay a $2,000 penalty for each employee, beyond the first 30 workers, who qualifies for subsidies and does not have employer coverage.[1] Part-time workers also count toward the number of employees in the firm (and thus toward the 50-employee threshold), but the government does not penalize firms for not offering them qualifying insurance.” (from the Heritage Foundation, but widely available. Other good articles are here and here.). So hiring employee number 50 carries a potential cost of 20 employees (50-30) times $2,000 per employee fine equals $40,000. That could easily be more than the actual wages paid to the 50th employee. I suspect there will be many firms that will simply stop hiring once they get to 45 employees.
Finally, consider the impact of the 2.3% tax on medical device sales in the U.S. scheduled to go into effect January 1. Medical device companies have announced layoffs as a direct result of this tax. Stryker Corp. will lay off about 100 workers at their Kalamazoo, Michigan offices and about 1,000 worldwide. I’ve written about this a couple of times before (here and here). It’s a shame that many so-called economists couldn’t manage to predict this.
Conclusion
Ironically, the ACA may actually reduce the unemployment rate. By cutting hours, businesses may well need more employees. But the underemployment rate is already intolerably high and will undoubtedly rise. Perhaps we need to revise Arthur Okun’s misery index (the sum of the unemployment rate and the inflation rate). I propose something simple: the sum of the unemployment rate, the inflation rate and half the difference between the underemployment rate and the unemployment rate. Details on this Enhanced Misery Index (EMI, as if we needed another TLA) will be forthcoming in a future article.
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[1]The portion of the health care law on the employer mandate explicitly states that firms with more than 50 employees are required to offer “full-time employees (and their dependents) the opportunity to enroll in minimum essential coverage under an eligible employer sponsored plan.” The Department of Treasury, however, has issued a proposed rule linking both the affordability of employer-sponsored insurance and compliance with the individual mandate to single coverage only. Since the employer is not penalized unless an employee enrolls in the exchanges, it is possible that if this proposed rule is adopted, employers will either drop family coverage or be indifferent to the affordability of the workers’ family coverage. Alternatively, the government may ultimately require employers to provide family coverage to workers with dependents. See Patient Protection and Affordable Care Act, Public Law 111-148, § 4980H.
The Consumer Spending Cliff
Posted by TonyLima in Current issues, The state of the economy on December 10, 2012
This morning’s Wall Street Journal brings us our graph of the day, possibly the month. Apparently the U.S. has already fallen off the consumer spending cliff. From that article:
But even after taking Sandy into account, October data were disappointing. Retail sales and consumer spending both fell. Personal income would have risen without Sandy, but barely, the Commerce Department said.
Take a look at this graph (from the article linked above):
Consumer confidence fell by ten full percentage points between November and December. Gee, I wonder what event happened that triggered such a decrease? Oh, yeah, the election. Voters may have elected President Obama, but markets are not happy. Apparently consumers are not very happy either. Real consumer spending (adjusted for inflation) fell slightly in October. The November results will be reported on December 21. Here are the most recent four months for the U.S.
| JUL | AUG | SEP | OCT |
| 109.099 | 109.063 | 109.523 | 109.188 |
Not much either way there. I predict a plunge in the December numbers — which won’t be available until January.
The Labor Force Participation Rate (LFPR): A Closer Look
Posted by TonyLima in Labor Market Issues 2000 to 2012, The state of the economy on July 5, 2012
In this post I’ll take a closer look at the labor force participation rate (LFPR). Specifically, I will show that the Great Recession has had dreadful consequences for younger workers. The U.S. is on the verge of losing a generation of young adults who simply cannot find work and have given up looking. And, at the same time, I’ll debunk one widespread myth: that the LFPR is declining because baby boomers are retiring. In fact, a quick look at the data reveals exactly the opposite: the LFPRs for older workers are rising, not falling.
The graphs below were compiled from data at the Bureau of Labor Statistics. Curiously, the only data I could find on the participation rate did not go back very far. Luckily, the BLS provides breakdowns of both the civilian noninstitutional labor force and total population by age bracket all the way back to 1960. So, with the help of our old pal Mr. Excel™, we can grow our own participation rate. (As always, I value transparency. Click here to download the Excel workbook with all the data. For technical reasons, this file is in Excel 2007 format.)
Background on the Labor Force Participation Rate
If you’ve been reading this blog for a while you know that I’ve written about the labor force participation rate before. The definition is simple: the number of people in the labor force divided by total population. The major complicating factor is the definition of the labor force. To be in the labor force someone must be either employed or unemployed. They are counted as employed if they did any work for pay during the previous four weeks. This is complication number one: many people counted as employed have part-time jobs, but would like to have full-time jobs. The unemployed are those who did no work for pay during the previous four weeks and are actively seeking a job. Those who have given up looking for work are not part of the labor force. A significant increase in the number of these discouraged workers leads to a drop in the labor force participation rate.
The LFPR has been declining since the mid‑1990s. There was an uptick in the mid‑00′s. But today the LFPR is at the lowest level since 1988. This is important. People who are unemployed for an extended period have increasing difficulty obtaining jobs. Indeed, there is quite a bit of evidence that their working skills deteriorate.[1] In general, the longer someone is out of work, the harder it becomes for them to find a job and, if successful, keep the job after returning to work. After we look at the data, I’ll summarize the research.
The Data
Before we get to the data, a word of warning. The vertical axes on the graphs have different scales. Since some groups have LFPRs in the neighborhood of 80% while others are down around 10%, I decided this was the best way to proceed. If you don’t like what I’ve done, download the Excel file and DIY.
Let’s begin by looking at the LFPR over the last 53 years.
The U.S. LFPR hit a low of about 54% in 1975, reflecting baby boomers into the population over age 16. So let’s look at what’s happened to the 16-24 age group:
This is where the catastrophe begins. Between 2000 and 2011 the LFPR for this group fell from about 66% to 55%. While I would like to believe this reflects more people going to college and even graduate school, I worry that is not the case. If this precipitous decline is being caused by people dropping out of the labor force because they cannot find work, we are condemning our youngest generation to an increased likelihood of reduced lifetime earnings.
Well, that’s depressing. Let’s look at ages 25 to 34:
The news here is a little better, but only a little. The LFPR has declined from 85% to about 81%. At least the decline isn’t as great as it is for younger workers. The next age group is 25-34:
That’s more like it. The age group 25-44 is actually doing pretty well. These are the folks who have jobs or are looking aggressively. What about 45-54?
Holding steady at about 82%.
Rather than discussing the last two groups individually, let’s look at both graphs:
Aha! People over age 54 are staying in the labor force longer. I should know — I’m one of them.
This is even more bad news Younger workers are dropping out of the labor force and, to a certain extent, they are being replaced by older workers hanging on to their jobs. This does not bode well for the future. Stay tuned to these pages. In the coming weeks I’ll look at the duration of unemployment broken down the same way.
Summary of the Economic Research
The most concise summary of the effects of long-term unemployment is from Aaronson, Mazumder, and Schechter (2010):
” As we entered 2010, the average length of an ongoing spell of unemployment in the United States was more than 30 weeks—the longest recorded in the post-World War II era. Remarkably, more than 4 percent of the labor force (that is, over 40 percent of those unemployed) were out of work for more than 26 weeks—we consider these workers to be long-term unemployed. In contrast, the last time unemployment reached 10 percent in the United States, in the early 1980s, the share of the labor force that was long-term unemployed peaked at 2.6 percent. Although there has been a secular rise in long‑term unemployment over the last few decades, the sharp increases that occurred during 2009 appear to be outside of historical norms. Further, this trend may present important implications for the aggregate economy and for macroeconomic policy going forward.
The private cost of losing a job can be sizable. In the short run, lost income is only partly offset by unemployment insurance (UI), making it difficult for some households to manage their financial obligations during spells of unemployment (Gruber, 1997; and Chetty, 2008). In the long run, permanent earnings losses can be large, particularly for those workers who have invested time and resources in acquiring knowledge and skills that are specific to their old job or industry (Jacobson, LaLonde, and Sullivan, 1993; Neal, 1995; Fallick, 1996; and Couch and Placzek, 2010). Health consequences can be severe (Sullivan and von Wachter, 2009). Research even suggests that job loss can lead to negative outcomes among the children of the unemployed (Oreopoulos, Page, and Stevens, 2008) and to an increase in crime (Fougère, Kramarz, and Pouget, 2009).
All of these costs are likely exacerbated as unemployment spells lengthen. The probability of finding a job declines as the length of unemployment increases. Although there is some debate as to exactly what this association reflects, it is certainly plausible that when individuals are out of work longer, their labor market prospects are diminished through lost job skills, depleted job networks, or stigma associated with a long spell of unemployment (Blanchard and Diamond, 1994). For risk-averse households that cannot insure completely against a fall in consumption as they deplete their precautionary savings, the welfare consequences of job loss rise as unemployment duration increases. Welfare implications are particularly severe during periods of high unemployment for individuals with little wealth (Krusell et al., 2008).”[2]
References
Aaronson, D., B. Mazumder, and S. Schechter (2010), ” What is behind the rise in long-term unemployment?” Economic Perspectives, Federal Reserve Bank of Chicago, 2Q/2010, 23-51.
Blanchard, O. and P. Diamond (1994), ” Ranking, Unemployment Duration, and Wages.” Review of Economic Studies (1994) 61, 417-434.
Thomsen, Stephan L. (2009), “Explaining the Employability Gap of Short-Term and Long-Term Unemployed Persons.” Kyklos, August 2009, v. 62, iss. 3, pp. 448-78.
Ochsen, C. and H. Welsch (2011), “The Social Costs of Unemployment: Accounting for Unemployment Duration.” Applied Economics, November 2011, v. 43, iss. 25-27, pp. 3999-4005.
[1] For examples, see Thomsen, Stephan L (2009); Ochsen, C. and H. Welsch (2011); Blanchard, O., and P. Diamond (1994); and Aaronson, D., B. Mazumder, and S. Schechter (2010).
[2] Aaronson, et. al., op. cit., p. 23.
Durable Goods Orders Fell Sharply in January
Posted by TonyLima in The state of the economy on March 1, 2012
Durable goods orders fell sharply in January. Dean Baker of CEPR pointed this out in his blog today. New orders for durable goods fell by an amazing 4.0% in January. This was a broadly based decline, ranging from -19.0% for nondefense aircraft and parts to -1.9% for e0lectrical equipment, appliances, and components. The only significant bright spot was defense capital goods which rose 17.7%.
As a service to readers, click here to download an Excel workbook with the raw data. I have created an additional worksheet that contains only the new orders data. (The original Census data combines shipments and new orders on a single worksheet, making the analysis of only new orders difficult.)
Thanks, Dean.
January Unemployment
Posted by TonyLima in Current issues, The state of the economy on February 3, 2012
The January unemployment rate was released this morning. Let’s get one thing out of the way right now. Last month I forecast 8.7% for January. The actual was 8.3%. “Forecasting is difficult, especially when it’s about the future.” – Nils Bohr
Economics is known as the dismal science. You’re about to learn why. How can a 0.2 percentage point decline in the unemployment rate be bad news? Read on.
First, every January the BLS updates their data for the civilian non-institutional population to align their data with information from the Census Bureau and other sources. Guess what? The bump to population was 1,685 thousand. At the same time the civilian labor force increased by 508 thousand. The labor force participation rate fell to 63.73%, the lowest level since 1979.
So apparently there were about 1.7 million folks that BLS thought were dead that were, in fact, alive. Some have argued that the decrease in the labor force participation rate is partly caused by the retirement of baby boomers. I wish. Everyone I know born after World War II is still working or looking for work.
Let’s look at changes between December, 2011 and January, 2012. The number of people unemployed fell by 339 thousand. Good news. And the number employed rose by 847 thousand, also good news. But 1,177 thousand people dropped out of the labor force. The employment – population ratio has remained virtually constant at 58.5% for the last three months. That means the gyrations between employment, unemployment, and labor force dropouts are just about offsetting each other.
When the unemployment rate drops mainly because an additional million people have left the labor force and population estimates are revised … well, let’s just say this report is not the sign of a healthy economy.
As always my work is an open book. Click here for the most recent Excel workbook.
Real Yields Turn Positive on 10-year TIPS
Posted by TonyLima in The state of the economy, Using economic data on January 20, 2012
On January 18, real yields turned positive on 10-year TIPS. Markets are possibly getting a bit more optimistic. Here are a couple of representative real yield curves. Essentially, during the last week the real yield curve shifted up about 14 basis points. (To get the complete data in an Excel 2007 workbook, click here.)
Nominal yields shifted up about 19 basis points in the longer maturities. Thus there was an increase in inflation expectations of about 5 basis points.
We can easily construct an inflation expectations curve. Expected inflation is simply the difference between the nominal and real interest rate for each maturity. Here’s what it looks like:
December Unemployment Rate
Posted by TonyLima in The state of the economy on January 6, 2012
The December unemployment rate was released this morning. The measured rate dropped to 8.5%. As always, a good part of this drop was caused by the ongoing decline of the number of workers in the labor force. The labor force participation rate dropped sharply to 64.1%. This is the lowest participation rate since 1983′s 64.0%. Anyone who argues that the drop in the unemployment rate signals an improving economy should be forced to recycle their Ph.D. in economics into aluminum beer cans. The graph below tells the gruesome story.
Unemployment Rates Revised for 2011
In other news, the Bureau of Labor Statistics issued revisions to the unemployment rate for the twelve months in 2011. The table below is from the BLS employment report.
|
Month |
As first computed |
As revised |
Change |
| January |
9.0 |
9.1 |
+0.1 |
| February |
8.9 |
9.0 |
+0.1 |
| March |
8.8 |
8.9 |
+0.1 |
| April |
9.0 |
9.0 |
0.0 |
| May |
9.1 |
9.0 |
-0.1 |
| June |
9.2 |
9.1 |
-0.1 |
| July |
9.1 |
9.1 |
0.0 |
| August |
9.1 |
9.1 |
0.0 |
| September |
9.1 |
9.0 |
-0.1 |
| October |
9.0 |
8.9 |
-0.1 |
| November |
8.6 |
8.7 |
+0.1 |
As I wrote last month, the November unemployment rate seemed too low. Frankly, however, I’d chalk that up to a lucky guess rather than any particular skill on my part.
As my fellow economist Dean Baker (co-director, Center for Economic Policy Research) noted in his Twitter feed (@DeanBaker13), some 42,000 of the new jobs were courier jobs, presumably seasonal hiring by FedEx, UPS, and other companies whose business increases sharply during December. However, the unemployment rate and most other statistics are seasonally adjusted. Let’s see if we can sort this out. (Even the BLS commented on this in their report, stating “Employment in transportation and warehousing rose sharply in December (+50,000). Almost all of the gain occurred in the couriers and messengers industry (+42,000); seasonal hiring was particularly strong in December.” The actual change from November to December in courier employment was an increase of 85,900 workers. The 42,200 figure cited by Baker uses the seasonally adjusted (SA) numbers. The 85,900 figure uses data that is not seasonally adjusted (NSA). Allow me a small digression.
Seasonal Adjustment
Most monthly and quarterly data is seasonally adjusted. The idea is to adjust for regular cyclical changes that occur every year. Two big causes of seasonality are holidays (Christmas, Kwanzaa, Hanukkah) and, well, the season. Ice cream sales rise in the summer. More products are shipped in November and December. Seasonal adjustment is designed to remove the regular changes that occur every year. These days seasonal adjustment is tricky because of the rapid changes in technology, demographics, and the economy itself. So the real question is how the difference between the seasonally adjusted and not seasonally adjusted figures for December, 2011 compare with historical data. I’ll add a technical note at the end of this post to explain how to find the exact data.
There’s another issue that came up looking at the data. The difference between the NSA and SA figures for December rose sharply beginning in 2006. The average difference between 2001 and 2005 was 15,620. From 2006 to 2011 the average was 40,200. Thus the NSA – SA figure more than doubled on average starting in 2006.
Statistical Results
Before I go any further, if you want the data, click here to download an Excel 2003 workbook. The last tab includes data sources and an explanation of how to extract this data from the bls.gov website.
What we really want to determine is whether the 60,900 difference between NSA and SA in December, 2011 is statistically different from the average. To do this we use a t-test. Calculating the standard deviation, then calculating (December – average)/standard deviation gives us a t-statistic. (Yes, I calculated the sample standard deviation.) Using data from 2006 – 2011 the t-statistic is 1.68. Not statistically significant at the five percent level. According to Stat Trek online the actual significance level is about 7.74%. Nice grey area result. (Naturally, using the entire sample period 2001 – 2011, the t-statistic improves to 2.06. Until someone can explain what happened in 2006, I’m sticking to 1.68.) For the masochists who like to see the numbers, here they are:
| Dec. year | Dec. SA (year) | Dec. NSA | Diff |
| 2011 | 567.80 | 628.70 | 60.90 |
| 2010 | 573.60 | 623.70 | 50.10 |
| 2009 | 561.30 | 594.70 | 33.40 |
| 2008 | 562.70 | 595.40 | 32.70 |
| 2007 | 588.00 | 620.80 | 32.80 |
| 2006 | 590.70 | 622.00 | 31.30 |
| 2005 | 579.70 | 591.10 | 11.40 |
| 2004 | 562.30 | 572.60 | 10.30 |
| 2003 | 542.60 | 568.00 | 25.40 |
| 2002 | 563.80 | 580.10 | 16.30 |
| 2001 | 577.50 | 592.20 | 14.70 |
| Average (2001-2005) | 15.62 | ||
| Average (all) | 27.91 | ||
| StdDev (all) | 15.98 | ||
| Average (2006-2011) | 40.20 | ||
| StdDev (2006-2011) | 12.35 | ||
| t (2011 – average all) | 2.06 | ||
| t (2011 – average 2006 and later) | 1.68 |
Another way of looking at this issue is to compare job gains from November to December with job losses from December to January. We’ll have to wait another month to get the full comparison for this season, but here’s the seasonally adjusted data from 2006 – 2010:
| Dec. year | Diff Jan – Dec SA | Diff Dec – Nov SA |
| 2011 | 42.20 | |
| 2010 | -48.70 | 46.30 |
| 2009 | -40.80 | 30.10 |
| 2008 | -5.30 | 13.40 |
| 2007 | -4.10 | 7.00 |
| 2006 | -8.40 | 1.30 |
For the last two years job losses in January have been greater than job gains in December. And remember, these are seasonally adjusted data. Unless the labor force continues to shrink at an alarming rate, the unemployment rate for January should tick up to about 8.7%. Remember, you read it here first.
One more item of note. The NSA – SA difference in December, 2010 was 50,100. In the four years before that, the difference was in the neighborhood of 35,000. Something is going on here. If I had to guess, I’d speculate that it’s the ongoing steady increase in online shopping. But wait — these are courier services, not UPS and FedEx. I look forward to comments from those smarter (and/or more imaginative) than me to clarify this mystery.
Note on Retrieving BLS Data
Start at http://www.bls.gov/data. Select the link to Employment data, then select the multi-screen data search column in the “Employment, Hours, and Earnings – National” row. On the next screen, check both the seasonally adjusted and not seasonally adjusted boxes, then click next form. Scroll down the “Supersector” list box until you see sector 43: Transportation and Warehousing. Click that, then next form. In the Datatype list box, click All Employees, Thousands, then click next form. Scroll down the Industry list box until you find 434920000 Couriers and messengers. Click that, then next form. The list box should have two series in it: CES4349200001 and CEU4349200001. CES is the seasonally adjusted data and CEU is not seasonally adjusted. Click retrieve data, then download the two Excel files that are generated.
There’s probably an easier way to get this data, but I’ve spent enough time trying to figure out how the BLS hides data.
Unemployment Drops to 8.6% — Can the News Really Be That Good?
Posted by TonyLima in The state of the economy on December 3, 2011
This morning the Bureau of Labor Statistics released the employment report for November. The good news is that the unemployment rate fell to 8.6%. Wow. But all is not rosy as we’ll see.
The headlines said the private sector added 140,000 jobs in November, but that was offset by a loss of 20,000 government jobs. Net gain: 120,000 jobs. Oh, really? Allow me a brief digression.
The BLS actually does two different surveys. The unemployment rate is based on the household survey, a large sample of U.S. households. But the jobs numbers cited in the headlines are from the establishment survey which surveys business and government. The two surveys often diverge, particularly during periods when unemployed workers are hanging out their own consulting shingles. The household survey includes these new businesses, but the establishment survey doesn’t know those new businesses exist (yet).
So let’s take a closer look at the household survey results. (Anyone who wants to play with the data should e-mail me for an Excel workbook containing seven of the BLS tables as well as a couple of tables I created for this blog.)
The number of people unemployed fell by 594,000. Of that, 315,000 were people who dropped out of the labor force. The civilian labor force fell from 154,198,000 to 153,883,000. According to the household survey, the number of people employed rose by 278,000. The sum of those two numbers (315,000 + 278,000) is equal to the change in unemployment (593,000, compared with the reported figure of 594,000, a difference most likely caused by rounding error).
Today the U.S. labor force participation rate is at the lowest level since the mid-1980s. Is it good news that so many people have given up looking for work? Some have taken early retirement, others have moved in with their families or friends. An 8.6% unemployment rate is good news, but not nearly as good as the headline writers would have you believe.
For the year 2010 the labor force participation rate was 64.7%. In November, that figure dropped even further to 64.0%. Remember, real GDP is equal to the number of workers times average worker productivity. The current increases in U.S. output are largely being fueled by huge productivity increases. As the U.S. moves increasingly toward a service producing information economy, the productivity of educated people will continue to soar. The question of what happens to the rest of the population remains unanswered.
GDP Growth Was Revised Down
Posted by TonyLima in The state of the economy on November 22, 2011
The second estimate is in for the third quarter and — no surprise — GDP growth was revised down. The preliminary estimate had been 2.5 percent, but the revised estimate is 2.0 percent. Remember, you read it here first.
It’s the Advance GDP Estimate, Stupid!
Posted by TonyLima in The state of the economy on October 27, 2011
Happy talk media today are whooping it up because real GDP grew by 2.5% in the third quarter. News flash: it’s the advance GDP estimate, stupid!
Don’t take my word for it. Read the first two paragraphs of the press release from the Bureau of Economic Analysis:
“Real gross domestic product — the output of goods and services produced by labor and property located in the United States — increased at an annual rate of 2.5 percent in the third quarter of 2011 (that is, from the second quarter to the third quarter) according to the “advance” estimate released by the Bureau of Economic Analysis. In the second quarter, real GDP increased 1.3 percent.
The Bureau emphasized that the third-quarter advance estimate released today is based on source data that are incomplete or subject to further revision by the source agency (see the box on page 3). The “second” estimate for the third quarter, based on more complete data, will be released on November 22, 2011.”
Prediction: this estimate will be revised downward twice — once at the end of November and a second time just as we’re about to welcome in 2012.

















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